Optimal cognitive radar transmit-receiver design for extended target with unknown target impulse response

In this paper, the problem of joint transmit waveform and receive filter design for cognitive radar (CR) is investigated. The problem is analyzed in signal-dependent interference, as well as additive channel noise for extended target with unknown target impulse response (TIR). An improved online waveform optimization design method is employed for target detection by maximizing the average signal to interference plus noise ratio (SINR) of the received echo on the premise of ensuring the TIR estimation precision. In the proposed method, the transmit waveform and receive filter are optimally determined at each step based on the observations in the previous steps. Simulation results demonstrate that CR with the proposed waveform achieve significantly higher rate of estimation accuracy and detection performance improvement compared to traditional radar system with fixed waveform, and offers more flexibility.

[1]  Amin Zia,et al.  Cognitive tracking radar , 2010, 2010 IEEE Radar Conference.

[2]  S. Haykin,et al.  Cognitive radar: a way of the future , 2006, IEEE Signal Processing Magazine.

[3]  M. Vespe,et al.  Lessons for Radar , 2009, IEEE Signal Processing Magazine.

[4]  Peng Wang,et al.  Adaptive waveform design for range-spread target tracking , 2010 .

[5]  Mark R. Bell Information theory and radar waveform design , 1993, IEEE Trans. Inf. Theory.

[6]  T. Naghibi,et al.  MIMO Radar Waveform Design in the Presence of Clutter , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[7]  S. Kay,et al.  Optimal Signal Design for Detection of Gaussian Point Targets in Stationary Gaussian Clutter/Reverberation , 2007, IEEE Journal of Selected Topics in Signal Processing.

[8]  Dante C. Youla,et al.  Optimum transmit-receiver design in the presence of signal-dependent interference and channel noise , 1999, Conference Record of the Thirty-Third Asilomar Conference on Signals, Systems, and Computers (Cat. No.CH37020).

[9]  Jiasong Mu,et al.  Throat polyp detection based on compressed big data of voice with support vector machine algorithm , 2014, EURASIP Journal on Advances in Signal Processing.

[10]  X. Zhang,et al.  Signal detection for cognitive radar , 2013 .